The widespread deployment of Autonomous Electric Taxis (AETs) in smart cities introduces critical challenges in large-scale dispatching and charging coordination under energy and operational constraints. Traditional Multi-Agent Reinforcement Learning (MARL) approaches often struggle to ensure both policy feasibility and system scalability in such complex, dynamic environments. In this paper, we propose a safe and scalable two-stage MARL framework for AET dispatching optimization. The proposed method, named Filter-to-Optimization Pipeline (FTOP), decouples constraint handling from reward maximization through a hierarchical architecture. In the first stage, an Action Classification-based Action Filter (ACAF) employs value decomposition to eliminate infeasible actions, enforcing energy and conflict constraints. In the second stage, a Utility-Prioritized Maximization Policy (UPMP) performs a model-based search within the filtered feasible space to optimize system-level utility. Extensive simulations demonstrate that FTOP achieves significant improvements in total fleet revenue, constraint satisfaction, and scalability across varying urban scenarios. These results highlight the potential of decomposed MARL strategies in solving large-scale, safety-critical optimization problems in autonomous transportation systems.

Traffic flow prediction plays a crucial role in intelligent transportation systems. The development of deep learning techniques has significantly improved the accuracy of traffic flow prediction; however, their limited generalization ability and lack of interpretability hinder their application in critical scenarios. Integrating physical knowledge into neural networks offers an effective solution to enhance both model generalization and interpretability, but embedding physical laws into complex traffic networks remains a challenge. Inspired by fluid dynamics principles, this paper proposes a novel framework for traffic flow prediction. Specifically, we first derive the physical constraints governing traffic flow based on the continuity equation of fluid mechanics. We then design a new feature extraction method to convert the derived physical constraints into spatiotemporal features that can be learned by neural networks. Finally, we introduce a deep learning framework that incorporates these physical features. Experiments on two real-world datasets, PEMS04 and PEMS08, demonstrate the effectiveness of the proposed method. The baseline model using our approach achieves MAE values of 16.69 and 12.34 on the two datasets, respectively, surpassing the current state-of-the-art models. Furthermore, ablation studies conducted with neural networks of varying parameter sizes further validate the robustness of the method in improving model performance. This paper offers a new perspective on integrating physical laws with data-driven approaches, enhancing both prediction accuracy and model interpretability.

The global imperative for carbon neutrality drives unprecedented transformation in building energy systems, where battery energy storage systems integrated with photovoltaic installations offer substantial demand-side flexibility. However, existing battery scheduling approaches—including heuristic rules, mathematical optimization, and reinforcement learning—fundamentally cannot incorporate user-informed future events lying outside historical training distributions, limiting their ability to leverage human foresight for proactive energy management. This work introduces a human-in-the-loop battery scheduling mechanism via intent-guided rule-reinforcement learning that systematically bridges user cognitive foresight with autonomous control systems. The mechanism employs large language models to extract structured battery pre-conditioning intents from natural language inputs through interactive dialogue, implements heuristic rules for time-bounded state preparation, utilizes Soft Actor-Critic reinforcement learning for baseline control, and coordinates these paradigms through hierarchical priority arbitration. Experimental validation using real residential energy data demonstrates substantial performance improvements: 39.2% cost reduction in discharge scenarios, 15.1% cost reduction in charge scenarios, and effective multi-intent coordination achieving 6.3% cost reduction with 20.0% improvement in cost savings compared to pure reinforcement learning approaches. This research establishes a new paradigm for building energy management where human knowledge becomes a strategic optimization resource.

Auction mechanism, as a fair and efficient resource allocation method, has been widely used in varieties trading scenarios, such as advertising, crowdsensoring and spectrum. However, in addition to obtaining higher profits and satisfaction, the privacy concerns have attracted researchers’ attention. In this paper, we mainly study the privacy preserving issue in the double auction market against the indirect inference attack. Most of the existing works apply differential privacy theory to defend against the inference attack, but there exists two problems. First, ‘indistinguishability’ of differential privacy (DP) cannot prevent the disclosure of continuous valuations in the auction market. Second, the privacy-utility trade-off (PUT) in differential privacy deployment has not been resolved. To this end, we proposed an attack-defense game-based reinforcement learning privacy-preserving method to provide practically privacy protection in double auction. First, the auctioneer acts as defender, adds noise to the bidders’ valuations, and then acts as adversary to launch inference attack. After that the auctioneer uses the attack results and auction results as a reference to guide the next deployment. The above process can be regarded as a Markov Decision Process (MDP). The state is the valuations of each bidders under the current steps. The action is the noise added to each bidders. The reward is composed of privacy, utility and training speed, in which attack success rate and social welfare are taken as measures of privacy and utility, a delay penalty term is used to reduce the training time. Utilizing the deep deterministic policy gradient (DDPG) algorithm, we establish an actor-critic network to solve the problem of MDP. Finally, we conducted extensive evaluations to verify the performance of our proposed method. The results show that compared with other existing DP-based double auction privacy preserving mechanisms, our method can achieve better results in both privacy and utility. We can reduce the attack success rate from nearly 100% to less than 20%, and the utility deviation is less than 5%. Note to Practitioners—Privacy protection in trading markets, such as advertising, crowdsensing, and spectrum, is crucial. Traditional approaches like differential privacy have been unable to entirely guard sensitive data against inference attacks. To address this, we introduce a novel privacy-preserving mechanism for double auction markets. Our approach employs an attack-defense game model, where noise is added to bidders’ valuations and then used to launch an inference attack. This process allows for the evaluation of the noise’s effectiveness and iteratively refines the privacy protection method. Transformed into a reinforcement learning model and optimized through a DDPG network, our mechanism reduces computational complexity. It has been shown to significantly diminish the success rate of inference attacks, while maintaining a minimal utility deviation. Practitioners in auction-based markets can leverage our approach to enhance privacy protection without negatively impacting market performance. By integrating our mechanism into their operations, auctioneers can foster a safer and more efficient trading environment.

In recent years, the rapid adoption of electric vehicles (EVs) in the taxi industry has transformed traditional taxi-hailing systems into electric taxi (E-taxi) hailing systems. As a result, it is crucial to develop effective strategies for optimizing E-taxi management by considering both passenger-taxi matching and charging planning. In this paper, we first formalize the E-taxi management optimization problem as a Markov decision problem with dynamic state and heterogeneous action. We then propose a dual-stage heuristic coordinated reinforcement learning (RL) approach that incorporates advanced feature selection and heuristic allocation strategies. Our approach consists of two main stages. In the first stage, we introduce the feature-guided state dimensionality stabilization proximal policy optimization (PPO) method to address dynamic state dimensions by a feature selection method, and enabling E-taxis to decide whether to charge or pick up passengers. In the second stage, we propose a heuristic coordinated assignment method to further allocate charging stations and passengers for the E-taxis, and provide the RL network in the first stage with rewards based on the results. This approach effectively tackles the challenge of RL processing of heterogeneous action spaces (charge and pick up). We evaluate our proposed method in a real-world E-taxi environment and find that it significantly enhances the experience for both E-taxis and passengers. Specifically, due to our method’s rational planning for passenger pick-up and charging, E-taxis can increase their revenue by 20% compared to traditional RL methods or random scheduling approaches. As for passengers, since the taxis have more efficiently planned their charging behavior, the probability of their orders being answered increases by 15%, while their waiting time is reduced by 55%. These achievements contribute to the advancement of E-taxi management strategies and promote the widespread adoption of electric vehicles, ultimately supporting the transition to a more sustainable transportation system. Note to Practitioners—The increasing adoption of electric vehicles in the taxi industry has led to the need for effective E-taxi management strategies that consider both passenger-taxi matching and charging planning. In this study, we introduce a dual-stage heuristic coordinated reinforcement learning approach that addresses these challenges by integrating a feature-guided state dimensionality stabilization proximal policy optimization method and a heuristic coordinated assignment method. Our approach offers several practical benefits for E-taxi service providers, drivers, and passengers. For E-taxi service providers, the proposed method improves E-taxi dispatch efficiency, resulting in a more effective use of available resources and potentially increasing overall revenue. For E-taxi drivers, our approach leads to better planning of charging and passenger pick-up decisions, increasing their earnings by 20% compared to traditional methods, and reducing the average occurrence of low battery status from more than 4 times every 10 hours to less than 1 time. Passengers, on the other hand, experience improved service quality due to the more efficient E-taxi management. The probability of their orders being answered increases by 15%, and their waiting time is reduced by 100%. These improvements contribute to an enhanced user experience and may encourage further adoption of E-taxis as a sustainable transportation solution. The proposed method can be integrated into existing E-taxi hailing platforms, such as DiDi and Uber, to enhance their dispatch and charging management capabilities. As the global trend towards sustainable transportation continues to grow, our approach provides valuable insights and a practical solution for the efficient management of E-taxi fleets in modern urban environments.

This paper investigates the compound control problem of aero-engine networked systems with multiple uncertainties and saturation constraints. To address these challenges, a novel sliding mode controller (SMC) with extended state observer (ESO) is first designed to implement anti-disturbance control, which can alleviate the chattering phenomenon without sacrificing robustness by two parallel ways: adaptive switching term and disturbance compensation. Then, the predictive control strategy is introduced to further optimize the reaching phase, and two disturbance-compensation-based predictive SMC schemes are proposed to coordinate diverse control requirements in the presence and absence of system constraints. When there are no saturation limits, the optimization problem is formulated as a convex one, and its analytic solution is derived explicitly. Furthermore, both state and control limits are considered in the system synthesis, and a standard prediction problem with multiple constraints is established. For high-frequency sampling needs, the Laguerre function is designed to reconstruct the input variables of the prediction sequence, which can effectively reduce the calculation complexity without compromising the dynamic performance. The experiment simulations show that the proposed compound schemes have strong robustness to multiple uncertainties and saturation constraints, and achieve promising control performance in both transient and steady-state phases.Note to Practitioners—As the heart of aircraft, aero-engine system is developing towards networked and intelligent. Subsequently, some new challenges are exposed to be addressed, such as communication delays, saturation constraints, electromagnetic disturbances, etc., which make the existing linear schemes fail to guarantee diverse requirements. Although SMC owns good robustness against multiple uncertainties, most relevant results still face some challenges, such as high switching gain, known disturbance boundary assumption, and sufficient control period. To address these difficulties, we propose two disturbance-compensation-based predictive SMC schemes to coordinate diverse control requirements with and without system constraints. The proposed schemes do not rely on excessive computing resources and are applicable to high-frequency sampling systems. The results of this paper can also provide guidance for the design of robust compound strategies for other networked systems with multiple uncertainties.

Nowadays location-based service (LBS) has become an important service in people’s daily life. Online taxi-hailing system (DiDi, Uber, etc.) is one of the most common LBS system. As the scale of online taxi services continues to expand, some problems have gradually emerged. Specifically, the uncertainty of taxis and passengers makes it difficult to match them effectively, and the passengers’ location privacy will be threatened from both internal and external of the system. In this article, we first proposed a Bayesian-based location privacy inference attack method from external adversary’s point of view. After that, an adaptive differential privacy-based dynamic incentive method was proposed. First, an adaptivity clocking area division method was proposed to resist the internal privacy threat. Second, a dynamic incentive bidding method was proposed to deal with the trading issue. Third, an exponential-based matching method was proposed to resist the external inference privacy threat. Further, theoretical proofs show that the proposed method satisfy the privacy properties of$k$-anonymity,$2~\mu _{1}\varepsilon $-differential privacy, and the economic properties of incentive compatibility, individual rationality. Finally, the experimental results show that the inference attack would achieve a maximum attack success rate of 95% while ensuring the accuracy within 150 m, and the proposed adaptive differential privacy-based dynamic incentive method can not only provide a good economic performance in terms of satisfaction ratio, social welfare, and travel distance, but also can resist internal privacy threat with less than 1% of privacy leakage probability and reduce the success rate of external privacy inference attacks to 25%.

Microgrids (MGs) are essential components of a smart grid, and they play an important role in assisting smart grid operation. Furthermore, multi-MG systems, which can further improve smart grid operation, raise significant challenges in terms of management and interoperation because of their increased complexity. To address this, we propose a multi-bid online incentive scheme for energy trading in multi-MG systems, in which MGs act as buyers and sellers to trade surplus energy. In the proposed system, buyer MGs can submit multiple bids to different sellers, and the distributed MG system operator matches buyer and seller MGs in a way that maximizes the utility of buyers. Theoretical analysis demonstrates that the proposed method meets the properties of incentive compatibility and individual rationality. Moreover, through detailed performance evaluation, the proposed scheme was capable of achieving better performance in comparison with existing schemes regarding utility, energy purchasing cost, and buyer MG’s satisfaction ratio. Our experimental results also demonstrated that the proposed scheme could shift the system peak-load, improving the robustness of the multi-MG system. Note to Practitioners—In this article, we addressed the energy trading issues in multi-microgrid (MG) system, and proposed a distributed incentive mechanism to achieve more flexible and efficient energy allocation. Most existing works on energy trading use centralized methods, which means all electrical users will submit only one bid to the platform, and finally the platform decides to match the trading parties. This will lead to users unable to actively choose trading partners, thus reducing the volume of the whole market. To this end, in this paper, we proposed a distributed incentive mechanism for the multiple microgrid system, in which the buyer microgrids are able to submit multiple bids to different sellers. Moreover, each seller microgrids act as a distributed energy market, and the distributed MG system operator matches the buyers and sellers which aims to maximize the utility of buyers. The proposed scheme is helpful in managing the energy trading among the multi-MG system and can be readily implemented in the real-world large-scale electricity trading market.

With the continuous expansion of the power grid nodes scale, traditional centralized state estimation method shows certain limitations in estimation efficiency and accuracy. Recently, some power grids adopt a distributed state estimation method, in which each partition independently estimates the partial state information by partitioning the entire power system. However, the deviation of the state estimation in certain partition will result in the deviation of the estimation results in the entire power grid system. In this paper, we propose the attack strategy against the distributed state estimation in smart grids from two perspectives, i.e. the attack against local physical measurement value of the power system partition and the attack against the measurement value of coordination center. Moreover, the theoretical analysis of the state estimation deviation caused by the proposed data integrity attack and the propagation processes of proposed attack vectors in measurement calculations are formalized. The effectiveness of the proposed attack strategy is verified in the IEEE-30 bus and IEEE-118 bus systems. Simulation results show that attacking against a certain partition of a distributed system can indirectly affect the state estimation results of other partitions and attacking against the measurement value of coordination center can directly threaten the state estimation results of the entire power grid. Note to Practitioners— This paper proposes two attack strategies against the distributed state estimation of power grid from two perspectives, i.e. the attack against local physical measurement value of the power system partition and the attack against the measurement value of coordination center. Most of the previous works fail to formalize the state estimation deviation of both partial and entire state estimation results of power grid after the attacker launches the attack against the distributed state estimation. We formalize the state estimation deviation caused by the proposed data integrity attack and the propagation processes of proposed attack vectors in measurement calculations. The effectiveness of the proposed attack strategy against the state estimation of power grid is verified in the IEEE-30 bus and IEEE-118 bus systems. Simulation results show that attacking against a certain partition of power grid can indirectly cause the deviation in the state estimation results of entire power grid and attacking against the measurement value of the coordination center can directly threaten the state estimation results of the entire power grid. In conclusion, the proposed attack strategies are helpful for the research community to design detection strategies in a targeted manner and can be conveniently applied to the real-world security management system of smart grid.

Smart meters, as a key component of Advanced Metering Infrastructure (AMI), collect fine-grained electricity consumption data for demand response in smart grids. While this data improves the grid’s accuracy, it also poses significant threats to users’ privacy. In this paper, we study the privacy issue in smart metering systems from both attacker and defender perspectives. First, we propose an improved Temporal Convolutional Network (TCN) based Non-Intrusive Load Monitoring (NILM) attack method, which infers electrical appliance usage from public load curves, addressing the gradient vanishing, gradient exploding, and other problems while improving attack accuracy. Second, we develop a rechargeable battery-assisted energy management system to hide load characteristics of electrical appliances by adding physical noise, thus resisting NILM attacks. To address the privacy-cost trade-off optimization problem, we propose a Practical Deep Reinforcement Learning-based Rechargeable Battery assist Privacy Preserving Method (PRoP) that learns optimal battery charging/discharging policies. We design a novel privacy measurement method and constraints to ensure the feasibility of system deployment and prove PRoP’s effectiveness in resisting NILM attacks. Comprehensive evaluations demonstrate that our improved TCN-based NILM method achieves an attack success rate of over 80% on various electrical appliances, improving attack performance (MAE, RMSE) by 20% compared to existing methods while reducing model training time. Moreover, our proposed PRoP achieves a better trade-off between privacy protection and electricity cost than existing battery-assisted methods, reducing costs by 5% and the attack success ratio to 36%, while increasing the MAE and RMAE obtained by NILM by 3 times.Note to Practitioners—Smart grid, which can support bidirectional information transmission, has a series of advantages, such as high efficiency and high stability. However, it also brings a significant threat to users’ electricity privacy. Although encryption-based privacy protection methods have been deployed on terminal devices of smart grids to prevent privacy leaks, this method can often only defend against intrusive attacks and has little effect on non-intrusive attacks. To this end, this paper studies the privacy issues caused by non-intrusive attacks. Specifically, to better study the protection method, we first investigate the attack mechanism and design an improved TCN-based Non-Intrusive Load Monitoring method. Then, we propose a Practical Reinforcement learning-based rechargeable battery-assisted Privacy preserving method (PRoP) to defend against this attack physically. The most practical contribution of this paper is that, compared with existing battery-assisted privacy protection methods, we do not blindly pursue algorithm performance but fully consider the practical factors of deployment, such as limiting battery capacity and constraining battery charging and discharging behavior. This can guide practitioners to better apply this technology in practice.

Due to high convenience and efficiency, electronic auction technology has been developed rapidly and has been applied to many online trading market applications. As more attention has been paid to information security, the privacy issues in the electronic auction have been widely studied. Differential privacy, as a lightweight privacy protection method, is an important direction in privacy preserving auction mechanism designing. However, most of the existing researches on differential privacy-based auction mechanism have not proposed a theoretical privacy inference attack method against the auction market. Therefore, the existence of privacy attacks is questionable, and the necessity and privacy protection performance of the existing differential privacy auction mechanism cannot be verified. To this end, in this paper we addressed the privacy attack issue and privacy protection issue in the auction market simultaneously. First, a Bayesian-based inference attack method against the double auction market was proposed from the perspective of the adversary. Theoretical analysis and evaluation results showed that the proposed inference attack method can effectively infer the bidding information of the target bidders, and attack success rate achieved approximately 95%. Second, an individual differential privacy-based auction mechanism was proposed from the perspective of the auction platform. Since not all the bidders will be attacked, we introduced the concept of individual differential privacy to provide targeted defense for specific bidders. Theoretical analysis demonstrated that the proposed auction mechanism satisfies$2\varepsilon $-individual differential privacy. And the extensive evaluation results showed that, compared with the existing differential privacy-based auction mechanism, our proposed mechanism provided the best privacy protection performance, that is, reduced the attack success rate to 20%, and ensured better auction performance, such as social welfare and satisfaction ratio, than the other mechanisms. Note to Practitioners—In this paper, we addressed the non-invasive privacy issues in the widely used electronic auction mechanism. Most of the previous works focused on designing differential privacy based auction mechanism against the non-invasive privacy attack, but neglecting the principle of non-invasive privacy attack methods. This makes it impossible to verify the privacy protection effectiveness of their proposed mechanisms. For this reason, a very large privacy budget may be selected to ensure the efficiency of privacy protection, which will lead to poor auction performance. To this end, we first proposed a Bayesian-based inference attack method against the double auction market. The proposed inference attack method allows the adversary infer the bidders’ private bidding information by the public auction results. Moreover, we then proposed an individual differential privacy auction mechanism, which aimed to achieve effective privacy protection while minimizing the added noise, thereby improving auction performance. The experiments demonstrate that the proposed Bayesian-based inference attack method achieves a good attack successful rate, and the proposed individual differential privacy auction mechanism will achieve the better efficiency of privacy protection as well as auction performance comparing with the exist differential privacy-based auction mechanism. In conclusion, this paper provides a verification method for the future research on the privacy protection of electronic auction mechanism. Meanwhile, this paper proposes an efficient privacy protection auction mechanism, which can be used in various trading scenarios.

An efficient energy scheduling strategy of a charging station is crucial for stabilizing the electricity market and accommodating the charging demand of electric vehicles (EVs). Most of the existing studies on energy scheduling strategies fail to coordinate the process of energy purchasing and distribution and, thus, cannot balance the energy supply and demand. Besides, the existence of multiple charging stations in a complex scenario makes it difficult to develop a unified schedule strategy for different charging stations. In order to solve these problems, we propose a multiagent reinforcement learning (MARL) method to learn the optimal energy purchasing strategy and an online heuristic dispatching scheme to develop a energy distribution strategy in this article. Unlike the traditional scheduling methods, the two proposed strategies are coordinated with each other in both temporal and spatial dimensions to develop the unified energy scheduling strategy for charging stations. Specifically, the proposed MARL method combines the multiagent deep deterministic policy gradient (MADDPG) principles for learning purchasing strategy and a long short-term memory (LSTM) neural network for predicting the charging demand of EVs. Moreover, a multistep reward function is developed to accelerate the learning process. The proposed method is verified by comprehensive simulation experiments based on real data of the electricity market in Chicago. The experiment results show that the proposed method can achieve better performance than other state-of-the-art energy scheduling methods in the charging market in terms of the economic profits and users’ satisfaction ratio.

Sharing private parking spaces during their idle time periods has shown great potential for addressing urban traffic congestion and illegitimate parking problems in smart cities. In this article, aiming to address the online parking-space sharing issue while ensuring the privacy of customer parking destination locations, we propose a novel destination privacy-preserving online parking sharing (DPOPS) incentive scheme. In particular, the online parking-space sharing problem is formalized as a social welfare maximization problem in a two-sided market, where parking-space providers (PSPs) and customers are regarded as sellers and buyers. Then, novel threshold value-based rules are designed to determine winners, payments, and reimbursement. Finally, winners are matched by solving a mixed-integer nonlinear programming problem, aiming to minimize the distance between customer’s destination and allocated parking space. In addition, the location privacy of the customers’ destinations is protected by the Laplace mechanism. We prove that DPOPS achieves several economically effective properties and approximate differential privacy. We analyze the upper bound of the efficiency loss of our scheme. Extensive evaluation results demonstrate that our scheme can not only achieve good performance regarding social welfare, PSP satisfaction ratio, privacy preservation, and computation overhead but also leads to shorter travel distances for customers comparing to the baseline scheme.Note to Practitioners—In this article, we address the online parking-space sharing issue with considering the parking-space providers (PSPs) and customers’ individual utility while preserving the location privacy of customers’ destinations. Most of the previous works focused on designing a centralized mechanism for allocating parking spaces without considering the protection of the customers’ location privacy. In particular, we propose an online parking-space sharing scheme called DPOPS, including a novel threshold value-based winner determination rule and a parking-space allocation rule. The proposed scheme DPOPS allows the PSPs and customers submit their bids and asks according to their own willingness and is able to improve the utilization of private parking spaces during their idle time periods. Moreover, the location privacy of customers’ destinations is protected by the Laplace mechanism. The experiments demonstrate that the proposed approach outperforms the exponential-based scheme in terms of PSP satisfaction ratio and the travel distance for parking-space customer. The proposed scheme is helpful in managing the vacant parking space in a competitive market and can be readily implemented in the real-world online parking-space sharing systems.

A smart grid integrates advanced sensors, efficient measurement methods, progressive control technologies, and other techniques and devices to achieve safe, efficient and economical operation of the grid system. However, the diversified and open environment of a smart grid makes energy and information of the smart grid vulnerable to malicious attacks. As a representative cyber-physical attack, the data integrity attack has an extremely severe impact on the grid operation for it can bypass the traditional detection mechanisms by adjusting the attack vector. In this paper, we first present the attack strategy against dynamic state estimation of power grid in the perspective of adversary and formulate the data integrity attack detection problem that has the characteristic of sequential decision making as a partially observable Markov decision process. Then, a deep reinforcement learning-based approach is proposed to detect against data integrity attacks, which utilizes the Long Short-Term Memory layer to extract the state features of previous time steps in determining whether the system is currently under attack. Moreover, the noisy networks are employed to ensure effective agent exploration, which prevents the agent from sticking to the non-optimal policy. The principle of a multi-step learning is adopted to increase the estimation accuracy of Q value. To address the sparse rewards problem, the prioritized experience replay is proposed to increase training efficiency. Simulation results demonstrated that the proposed detection approach surpasses the benchmarks in the comparison metrics: delay error rate and false rate.Note to Practitioners—In this paper, we present a deep reinforcement learning-based algorithm to defend against the data integrity attacks of smart grid. Most of the previous works discretized the system states and utilized the current state information to identify whether the system is under attack. For this reason, the detection policy may totally ignored the continuously changing characteristics of the grid states, which will lead to poor detection performance. Moreover, the attacked system states only accounts for a small part of the entire grid operation states, the probability of sampling the experience containing the attack state is extremely small, which limits the learning efficiency of previous RL-based detection approaches. In order to increase the accuracy of detection, we first present the attack strategy against power grid’s dynamic state estimation in the perspective of adversary and formulate the partially observable Markov decision process model of attack detection problem. Moreover, we propose a deep reinforcement learning-based detection approach combining the LSTM network to extract the system state features of the previous time steps to determine whether the system is currently being attacked. To address the sparse rewards problem, the prioritized experience replay is used to increase learning efficiency. The experiments demonstrate the effectiveness of proposed detection scheme compared with benchmarks in terms of detection delay as well as accuracy. In conclusion, the proposed detection scheme is helpful in defending against the data integrity attacks without obtaining the opponent’s strategy in advance and can be conveniently applied to the real-world security management system of smart grid.

Rank minimization is widely used to extract low-dimensional subspaces. As a convex relaxation of the rank minimization, the problem of nuclear norm minimization has been attracting widespread attention. However, the standard nuclear norm minimization usually results in overcompression of data in all subspaces and eliminates the discrimination information between different categories of data. To overcome these drawbacks, in this article, we introduce the label information into the nuclear norm minimization problem and propose a labeled-robust principal component analysis (L-RPCA) to realize nuclear norm minimization on multisubspace data. Compared with the standard nuclear norm minimization, our method can effectively utilize the discriminant information in multisubspace rank minimization and avoid excessive elimination of local information and multisubspace characteristics of the data. Then, an effective labeled-robust regression (L-RR) method is proposed to simultaneously recover the data and labels of the observed data. Experiments on real datasets show that our proposed methods are superior to other state-of-the-art methods.

The rapid and wide adoption of microgrids (MGs) and the increasing popularity of electric vehicles (EVs) have created a unique opportunity for the integration of these technologies. In this article, we address the issue of demand response of EVs during MG outages by leveraging Vehicle-to-Grid (V2G) technology. Particularly, we investigate an auction trading market that allows EVs with surplus energy to act as sellers, and EVs that want to be charged to act as buyers. A novel distributed double auction scheme is proposed to allow each buyer EV to submit multiple bids to seller EVs in different parking lots. Nonetheless, the locations of buyer EVs could be inferred by an adversary through analyzing the valuations, posing serious privacy and security risks. In this regard, a valuation-based attack scheme is investigated to validate the potential privacy risk. To defend against such an attack, we present a location privacy-preserving double auction scheme, in which the MicroGrid Central Controller (MGCC) acts as the auctioneer, solving the social welfare maximization problem of matching buyers to sellers, and the cloud is used to conduct calculations for the auctioneer, protecting the privacy of participants via homomorphic encryption. Theoretical analysis is conducted to validate our auction scheme in satisfying the designed economic and privacy properties (e.g., strategy-proofness and$k$-anonymity). The experimental results show that our auction scheme can not only mitigate the demand response problem in MGs, but also provides good performance with respect to social welfare, satisfaction ratio, computational and communication overhead, and privacy leakage.

Although representation learning (RL) has achieved great success in intelligent fault diagnosis, existing RL methods still have two deficiencies: 1) all learned bases are employed for fault diagnosis, which may degrade the computational efficiency and diagnosis accuracy and 2) it is unable to know which bases are more useful or less useful for fault diagnosis. In this work, we present a novel RL method, namely, sparse filtering with adaptive basis weighting (SFABW) whose architecture is a three-layer neural network. The first and the last two layers are responsible for basis learning and basis weighting, respectively. We formulate a loss function to model such architecture and develop an iterative algorithm to minimize it, also we prove the convergence of this algorithm in theory. Through optimizing the whole network, we are able to obtain a group of bases together with their weights simultaneously. A subset of top-ranked bases with great weights is retained while the rest bases are discarded. The experimental results on a motor bearing dataset and a gear dataset have demonstrated the effectiveness of our method.

The existing multi-agent reinforcement learning methods (MARL) for determining the coordination between agents focus on either global-level or neighborhood-level coordination between agents. However the problem of coordination between individual agents is remain to be solved. It is crucial for learning an optimal coordinated policy in unknown multi-agent environments to analyze the agent's roles and the correlation between individual agents. To this end, in this paper we propose an agent-level coordination based MARL method. Specifically, it includes two parts in our method. The first is correlation analysis between individual agents based on the Pearson, Spearman, and Kendall correlation coefficients; And the second is an agent-level coordinated training framework where the communication message between weakly correlated agents is dropped out, and a correlation based reward function is built. The proposed method is verified in four mixed cooperative-competitive environments. The experimental results show that the proposed method outperforms the state-of-the-art MARL methods and can measure the correlation between individual agents accurately.

Electric vehicle (EV) has become one of the most critical components in the smart grid with the applications of the Internet-of-Things (IoT) technologies. Real-time charging control is pivotal to ensure the efficient operation of EVs. However, the charging control performance is limited by the uncertainty of the environment. On the other hand, it is challenging to determine a charging control strategy that is able to optimize multiple objectives simultaneously. In this article, we formulate the EV charging control model as a Markov decision process (MDP) by constructing state, action, transition function, and reward. Then, we propose a deep-reinforcement-learning-based approach: charging control deep deterministic policy gradient (CDDPG) to learn the optimal charging control strategy for satisfying the user's requirement of battery energy while minimizing the user's charging expense. We utilize the long short-term memory (LSTM) network that extracts the information of previous energy price to determine the current charging control strategy. Moreover, Gaussian noise is added to the output of the actor network to prevent the agent from sticking into the nonoptimal strategy. In addition, we address the limitation of sparse rewards by using two replay buffers, of which one is used to store the rewards during the charging phase and another is used to store the rewards after charging is completed. The simulation results prove that the CDDPG-based approach outperforms the deep-$Q$ -learning-based approach (DQL) and the deep-deterministic-policy-gradient-based approach (DDPG) in satisfying the user's requirement for the battery energy and reducing the charging cost.

Due to the existing effects of intermittent jumps of unknown parameters during operation, effectively establishing transient and steady-state tracking performances in control systems with unknown intermittent actuator faults is very important. In this article, two prescribed performance adaptive neural control schemes based on command-filtered backstepping are developed for a class of uncertain strict-feedback nonlinear systems. Under the condition of system states being available for feedback, the state feedback control scheme is investigated. When the system states are not directly measured, a cascade high-gain observer is designed to reconstruct the system states, and in turn, the output feedback control scheme is presented. Since the projection operator and modified Lyapunov function are, respectively, used in the adaptive law design and stability analysis, it is proven that both schemes can not only ensure the boundedness of all closed-loop signals but also confine the tracking errors within prescribed arbitrarily small residual sets for all the time even if there exist the effects of intermittent jumps of unknown parameters. Thus, the prescribed system transient and steady-state performances in the sense of the tracking errors are established. Furthermore, we also prove that the tracking performance under output feedback is able to recover the tracking performance under state feedback as the observer gain decreases. Simulation studies are done to verify the effectiveness of the theoretical discussions.

In this paper, to address the issue of demand response in the smart grid with island MicroGrids (MGs), we introduce an effective and secure auction market that allows electric vehicles (EVs) having surplus energy to act as sellers, and the EVs having insufficient energy in the island MGs to act as buyers. There are two primary challenges in designing an effective auction market in the smart grid. First, the auction market scheme shall be online, allowing buyers and sellers to enter the market at any time, and satisfy several critical economic properties (individual rationality, incentive compatibility, and so on.). Second, the sensitive information of participants shall be protected in the auction process. To address these challenges, we present a novel privacy-preserving online double auction scheme based on differential privacy. In our auction market, the MicroGrid Center Controller (MGCC) acts as the auctioneer, aiming at solving the social welfare maximization problem to match buyers and sellers. The principle of differential privacy is leveraged to protect the privacy of EVs' sensitive bidding information. Via theoretical analysis, we demonstrate that our designed auction scheme satisfies both economic and privacy-preserving properties, including individual rationality, incentive compatibility, weak budget balance, and ε-differential privacy. We conduct an extensive performance evaluation to measure the effectiveness of our proposed scheme. Our experimental results show that the proposed auction scheme can not only ensure the privacy of participants but also effectively facilitates demand response in the smart grid, with respect to social welfare, satisfaction ratio, social efficiency, and computational overhead.

Effectively compensating unknown intermittent actuator faults in uncertain decentralized nonlinear systems is a very difficult problem, and very few results have been obtained. In this article, to address this issue, an adaptive neural output feedback compensation control scheme based on command-filtered backstepping is developed. First, we design a bank of observers to estimate the system states and utilize neural networks with random hidden nodes to approximate the unknown functions of these observers. Second, a smooth projection algorithm is used to online update estimated parameters in the controllers such that the possible ceaseless increase in the estimated parameters caused by intermittent actuator faults can be eliminated. Due to the presence of intermittent jumps of unknown parameters, a modified Lyapunov function is developed to analyze the system stability. It is proved that the boundedness of all closed-loop system signals is ensured and the ultimate bound of the tracking error depends on design parameters, adjustable jumping amplitude of Lyapunov function, and minimum fault time interval. Third, by analyzing the system transient performance, the peaking phenomenon at the starting instant of the system operation can be removed, and a root mean square type of bound is established to illustrate that the transient tracking error performance is tunable by design parameters. Finally, simulations studies are done to illustrate the effectiveness of the theoretical results.

In this paper, we address the issue of demand response (DR) in microgrids via vehicle-to-vehicle technology in the smart grid with consideration for location privacy protection supported by Internet of Vehicles. To enable effective DR, the online double auction is a viable approach to support energy trading between electric vehicles (EVs) that have surplus or insufficient energy, while the utility of each participant can be considered. Nonetheless, there are three primary challenges in designing such an online double auction approach. First, as EVs are allowed to enter the market at any time, the auctioneer should make the best decision without further information about bids and asks. Second, as EVs are allowed to enter the market in different places, the auctioneer should perform routing optimization for EV charging after determining the winner. Third, there is a risk of leakage in the location of EVs that needs to be protected. To tackle these issues, we present a new truthful online double auction scheme, which features multiunit energy trading among EVs, routing optimization for EV charging, and location privacy protection. We conduct a theoretical analysis and demonstrate that our online double auction scheme is capable of achieving several important economic properties as well as the privacy guarantee (i.e., k-anonymity). Our experimental results show that the proposed scheme can achieve good performance with respect to social welfare, satisfaction ratio, total profit of EV owners, peak load shifting, state of charge, driving distance satisfaction, and computing time, and can further ensure location privacy protection.

In this paper, we address the issue of the energy trading in the scenario of electric vehicles (EVs) charging in the smart grid. The EVs energy trading problems have attracted growing attention with the popularity of EVs. As the traditional first-reserve-first-serve scheme in the energy trading market impairs the benefits of both buyers and seller, we consider an auction scheme, called progressive second price (PSP), which has been proved to be an efficient way to conduct resource allocation in the trading market. Compared with other auction schemes, the PSP scheme can achieve both incentive compatibility and Nash equilibrium, which are important properties for the market. Nonetheless, the PSP auction scheme is not designed for online auction and it cannot guarantee that the seller can provide an enough number of charging piles to satisfy the demand of winners. To tackle these issues, in this paper we propose a novel online continuous PSP-based auction scheme, which is capable of not only achieving the property of online energy trading but also guaranteeing that the number of winners is limited to be no more than the number of charging piles. Further, we prove that our auction scheme achieves incentive compatibility and Nash equilibrium. The extensive experimental results demonstrate that our auction scheme achieves good performance with respect to social welfare, the seller satisfaction ratio, the buyer satisfaction ratio, as well as computation overhead.

With the development of online taxi-hailing systems (DiDi, Uber Lyft, etc.), how to effectively allocate taxis has attracted great attention in the recent past. Meanwhile, with the rapid increase of taxi-related crimes, the privacy of passengers' sensitive information such as location remains a critical concern. In this paper, we present a novel incentive-based scheme, which provides the differential privacy guarantee for passengers' locations in taxi-hailing systems. To be specific, to allocate limited taxis to passengers, we first present the Vickrey-Clarke-Groves (VCG)-based online auction mechanism for determining the winning passengers. Then, to match the taxis and winning passengers as well as to protect the location privacy of passengers, we present the new allocating rule based on the exponential differential privacy-based mechanism. Further, we prove that the proposed incentive-based scheme satisfies both economic properties and 2-ε differential privacy guarantee. Finally, we evaluate the performance of our proposed scheme. The experimental data confirms that our proposed scheme not only achieves better performance than the two baseline schemes with respect to social welfare and satisfaction ratio, but also is capable of protecting the location privacy of passengers with low privacy disclosure.

In this paper, we present theory and a design of the online double auction for the trading of energy within a smart grid with microgrids (MGs). The online double auction has the potential to enable the allocation of surplus electricity to the MGs that need electricity with the highest gain in the real-time market. Nonetheless, two critical issues remain challenging when designing an effective online double auction scheme in such a system. First, as the agents are allowed to arrive and depart at any time, the auctioneer needs to make decisions without the information of further bids and asks. Second, the economic properties of strategy-proof, individual rational, and (weak) budget balance should be satisfied. To address these issues and enable multiunit electricity trading among local MGs, in this paper, we propose a strategy-proof online double auction (SODA) scheme, in which the surplus and insufficient MGs in the system are treated as sellers and buyers, respectively, and the MG center controller is capable of maximizing the social welfare of MGs by appropriately matching buyers and sellers. Via theoretical analysis, we prove that SODA can achieve the properties of individual rationality, (weak) budget balance, strategy-proofness, and computational efficiency. Experiments also show that SODA is capable of reducing the energy purchasing cost of the MGs and shifting the peak-load, while achieving great performance with respect to social welfare, seller/buyer satisfaction ratio, social efficiency, and computation overhead.

In this paper, we investigate the data integrity attack against Optimal Power Flow (OPF) with the least effort from the adversary's perspective. The investigated attack can first select the minimum number of target nodes to compromise by analyzing the difference between the capacity of transmission line and the real transmission power, and then search for a critical attack vector (with a goal to minimize the amount of information to manipulate) as an optimal attack strategy. To defend against such an attack, we develop the defensive scheme by protecting the critical nodes. Based on various IEEE standard systems, we show the effectiveness of our investigated attack scheme and the corresponding defense schemes.

In this paper, we address the issue of data trading in big data markets. Data trading problems have attracted increased attention recently, as the economic benefits and potential of big data trading are substantial and varied. However, how to effectively trade data between the data owners (sellers) and data collectors/users (buyers) is far from settled, and requires careful design. Auction mechanisms have been applied across many fields, and have significant potential to facilitate data transactions in a fair, truthful, and secure way. Nonetheless, a truthful auction must ensure the property of incentive compatibility, meaning that the bidders can obtain highest utility if and only if they submit their bids and asks truthfully. Furthermore, a truthful and fair auction should also protect the optimal auction results from being manipulated by false-name bidding attacks, where users (participants) utilize multiple identities or accounts to influence the auction results. To tackle these issues, we propose a Multi-round False-name Proof Auction (MFPA) scheme, which enables data trading among data owners (sellers) and data collectors (buyers). We prove that our MFPA scheme achieves the properties of incentive compatibility, false-name bidding proofness, and computational efficiency. The experimental results demonstrate that MFPA achieves good performance in terms of social surplus, satisfaction ratio, and computation overhead.

In this paper, we address the problem of demand response of electrical vehicles (EVs) during microgrid outages in the smart grid through the application of Vehicle-to-Grid (V2G) technology. Particularly, we present a novel privacy-preserving double auction scheme. In our auction market, the MicroGrid Center Controller (MGCC) acts as the auctioneer, solving the social welfare maximization problem of matching buyers to sellers, and the cloud is used as a broker between bidders and the auctioneer, protecting privacy through homomorphic encryption. Theoretical analysis is conducted to validate our auction scheme in satisfying the intended economic and privacy properties (e.g., strategy-proofness and k-anonymity). We also evaluate the performance of the proposed scheme to confirm its practical effectiveness.

Microgrids (MGs) have attracted growing attention due to self-sufficiency and self-healing properties. Nonetheless, the intermittent nature and uncertainty of distributed energy resources and load demands remain challenging issues in balancing demands and managing energy resources in MGs. Existing research efforts mainly focus on developing techniques to enable interactions between local MGs and the utility grid, which leads to high line power losses and operation costs. In this paper, we present the Sto2Auc framework to address the issue of stochastic optimal bidding problem for a system with MGs. First, the optimal bidding problem is formulated as a two-stage stochastic programming process, which aims to minimize the system operation cost and obtain optimal energy capacity of MGs by the MG center controller (MGCC). Uncertainties arise from both energy supply and demand, which are considered in the stochastic model, and random parameters representing those uncertainties are captured by using the Monte Carlo method. Second, to enable optimal electricity trading between the insufficient and surplus MGs, we propose a distributed double auction (DDA)-based scheme, which is proven to converge to the optimal social welfare of the system with MGs, and achieves the economical properties of being strategy-proof, individually rational, and (weak) budget balanced. Extensive experiments on an MG system composed of IEEE-33 buses demonstrate the effectiveness of proposed scheme. The experimental results show that Sto2Auc framework is capable of reducing the operational cost of MG systems, while the implemented DDA scheme achieves good performance with respect to social welfare, demand insufficiency, and MGCC profit.

In this paper, we address the security issue of optimal power flow (OPF) (as a key component in the smart grid). To be specific, we investigate the data integrity attack against OPF with the least effort from the adversary's perspective, and propose effectively defense schemes to combat the data integrity attack, with respect to the number of nodes to compromise and the amount of information to manipulate. The investigated attack can first select the minimum number of target nodes to compromise by analyzing the difference between the capacity of transmission line and the real transmission power, and then search for a critical attack vector as an optimal attack strategy. To defend against such an attack, we develop the defensive schemes by not only protecting the critical nodes but also detecting the existence of attacks based on false measurement detection schemes. Based on various IEEE standard systems, we show the effectiveness of our investigated attack scheme and the corresponding defense schemes. The experimental results show that the discovered compromised nodes and critical attack vector could lead to the increase of the fuel cost from the power generation by compromising the least number of nodes and injecting the least amount of false information, in comparison with the random attack as the baseline attack strategy. In addition, our two developed defensive schemes are capable of making OPF resilient to the data integrity attack via protecting critical nodes and identifying the falsified measurements accurately in the system.

State estimation plays a critical role in self-detection and control of the smart grid. Data integrity attacks (also known as false data injection attacks) have shown significant potential in undermining the state estimation of power systems, and corresponding countermeasures have drawn increased scholarly interest. Nonetheless, leveraging optimal phasor measurement unit (PMU) placement to defend against these attacks, while simultaneously ensuring the system observability, has yet to be addressed without incurring significant overhead. In this paper, we enhance the least-effort attack model, which computes the minimum number of sensors that must be compromised to manipulate a given number of states, and develop an effective greedy algorithm for optimal PMU placement to defend against data integrity attacks. Regarding the least-effort attack model, we prove the existence of smallest set of sensors to compromise and propose a feasible reduced row echelon form (RRE)-based method to efficiently compute the optimal attack vector. Based on the IEEE standard systems, we validate the efficiency of the RRE algorithm, in terms of a low computation complexity. Regarding the defense strategy, we propose an effective PMU-based greedy algorithm, which cannot only defend against data integrity attacks, but also ensure the system observability with low overhead. The experimental results obtained based on various IEEE standard systems show the effectiveness of the proposed defense scheme against data integrity attacks.

In the smart grid, as a large-scale distributed cyber-physical system, Demand Response (DR) plays an important role in the electricity market. Various demand response schemes have been developed to improve the efficiency and economy of power utilization. Nonetheless, most existing schemes, including both market-led and system-led schemes, do not carefully take the information security into account in the DR process so that the power grid could suffer from cyber attacks (data integrity attacks, etc.). To address this issue, in this paper we proposed a resilient demand response scheme based on microgrids, which can achieve both great effectiveness of energy use and security resilience against data integrity attacks. In our scheme, the DR process considers two power distribution stages. In the intra-microgrid stage, the DR providers generate the list of possible electricity prices and schedules for power delivery. In the inter-microgrid stage, utilities select the proper electricity price and the schedule for power delivery. In this way, the damage impact of attacks on the power grid can be limited only within isolated microgrids that are compromised, while other microgrids that are not compromised can operate effectively. Our experimental results show that our scheme can not only bring better benefits to all participants, but also achieve a greater security resilience in the DR process in comparison with existing schemes.

Islanded Microgrids (IMG) have attracted much attention in the research and development of the smart grid, which is a large-scale distributed cyber-physical system. To overcome the limitations of the single IMG on energy efficiency and economic, In this paper, we first proposed a novel self-sufficient system, namely “Archipelago microgrid (MG)”, which is comprised of multi-microgrids while disconnected with the utility grid. We formalized the EV (electric vehicle) penetration problem as an optimization mixed integer nonlinear programming, which aims to minimize the emission and operation cost in the system. To enable a reasonable deployment of EV in each MGs, we developed two scheduling schemes, namely Unlimited Coordinated Scheme (UCS) and Limited Coordinated Scheme (LCS), respectively. A decentralized algorithm was also developed to solve the optimization model in LCS. A simulation study based on an modified IEEE-9 bus system with three MGs show that our proposed schemes can reduce both the environmental pollution created by CO2 emission and operation cost. Especially, with the consideration of peak load limits and resident preferences, the LCS scheme can obtain better results than the UCS scheme, leading to the reduction of the environmental pollution by 15.2% raised by CO2 emission, as well as the total cost by 10.8% in the system.

Abstract State estimation is a very critical component in smart grid, a typical energy‐based cyber‐physical system. Kalman filter has been widely used in the dynamic state estimation of power systems. Although a large number of research efforts have been made on the robustness and filtering effectiveness, little effort has been conducted on cyber attacks against Kalman filtering. To address this issue, in this paper we systematically compare three representative Kalman filtering techniques and formalize the problem of anomaly detection against false data injection attacks in Kalman filter. On the basis of our modeling results, we investigate five novel attack approaches that can bypass the anomaly detection. To defend against those attacks, we develop two countermeasures: the enhancement of Kalman filtering and the temporal‐based detection algorithm. We conduct extensive performance evaluation and our data validates our theoretical finding well. Copyright © 2013 John Wiley & Sons, Ltd.

In an intelligence transportation system (ITS), to increase traffic efficiency, a number of dynamic route guidance schemes have been designed to assist drivers in determining the optimal route for their travels. In order to determine optimal routes, it is critical to effectively predict the traffic condition of roads along the guided routes based on real-time traffic information to mitigate traffic congestion and improve traffic efficiency. In this paper, we propose a Dynamic En-route Decision real-time Route guidance (DEDR) scheme to effectively mitigate road congestion caused by the sudden increase of vehicles and reduce travel time. Particularly, DEDR considers real-time traffic information generation and transmission. Based on the shared traffic information, DEDR introduces Trust Probability to predict traffic conditions and dynamically en-route determine alternative optimal routes. In addition, DEDR considers multiple metrics to comprehensively assess traffic conditions and drivers can determine optimal route with individual preference of these metrics during travel. DEDR also considers effects of external factors (e.g., Bad weather, incidents, etc.) on traffic conditions. Through a combination of extensive theoretical analysis and simulation experiments, our data shows that DEDR can greatly increase the efficiency of an ITS in terms of great time efficiency and balancing efficiency in comparison with existing schemes.

The smart grid is a new type of energy-based cyber-physical system (CPS), which enables interactions between the utility provider and customers through smart meters and advanced metering infrastructures (AMI). Nonetheless, an adversary can inject misleading energy usage information to the utility provider through compromised smart meters and disrupt the grid and electricity market operations. To address this issue, in this paper, we propose an Energy Dispatching False Data Defense (EDF2D) approach, which can effectively detect the forged interactive information between customers and the utility provider with a great accuracy and mitigate the damage raised by attacks on grid operations. Particularly, EDF2D uses the historical interactive information of normal users to determine the conditional probabilities of data anomalies. Based on these conditional probabilities, a Bayesian network designed for detecting false data can be established by EDF2D, and this network is then used to confirm the authenticity of interactive information received by the utility provider originally transmitted from customers. Through a combination of theoretical analysis and performance evaluation, our experimental data shows that EDF2D can effectively detect harmful false interactive data forged by the adversary and mitigate false data injection attacks on smart grid operations.

In this paper, we addressed the issue of a stochastic optimal bidding problem for a system with microgrids (MGs). The optimal bidding problem is formulated as a two-stage stochastic programming process, which aims to minimize the system operation cost and to expand energy interactions among local MGs that are geographically close. Uncertainties come from both energy supply and demand sides (e.g., wind, solar, and load demand) are considered in the stochastic model and random parameters to represent those uncertainties are captured by using the Monte Carlo method. To enable an optimal electricity trading between local MGs, we presented two bidding schemes: (i) Cournot equilibrium based Dynamic Backtrack Energy Trading (DBET), and (ii) double auction based Dual Decomposition Auction (DDA). Experimental results on an IEEE-33 bus based system with MGs were presented to show the effectiveness of our proposed schemes. Experimental results show that our proposed bidding schemes can reduce the operation cost of the system, while the DDA scheme achieves better performance in terms of system social welfare than the DBET scheme.

It is critical for a power system to estimate its operation state based on meter measurements in the field and the configuration of power grid networks. Recent studies show that the adversary can bypass the existing bad data detection schemes, posing dangerous threats to the operation of power grid systems. Nevertheless, two critical issues remain open: 1) how can an adversary choose the meters to compromise to cause the most significant deviation of the system state estimation, and 2) how can a system operator defend against such attacks? To address these issues, we first study the problem of finding the optimal attack strategy--i.e., a data-injection attacking strategy that selects a set of meters to manipulate so as to cause the maximum damage. We formalize the problem and develop efficient algorithms to identify the optimal meter set. We implement and test our attack strategy on various IEEE standard bus systems, and demonstrate its superiority over a baseline strategy of random selections. To defend against false data-injection attacks, we propose a protection-based defense and a detection-based defense, respectively. For the protection-based defense, we identify and protect critical sensors and make the system more resilient to attacks. For the detection-based defense, we develop the spatial-based and temporal-based detection schemes to accurately identify data-injection attacks.

The smart grid shall not only integrate the intermittent resources (IRs) to meet the diverse demands of users and reduce the greenhouse gas emission, but also integrate Electricity Vehicles (EVs) as the energy storage facility to smooth the bulk power generation over time. In this paper, we model and analyze the impact of integrating IRs and EVs on the bulk power generation in the smart grid. In particular, we introduce the reliability ratio to quantify the power generation capacity of intermittent resources and model the process of charging and discharging of EVs as a queuing system. We extend the Security-Constrained Economic Dispatch (SCED) and include the reliability limit of IRs and the number of EVs in the power generation dispatch process and formally analyze the effect of IRs and EVs on the bulk power generation. We conduct extensive simulation and our data shows that increasing IRs can decrease the bulk generation and the curve of bulk generation over time becomes smooth as the number of EVs increases.

The smart grid poses great concern about the exposure of consumers' privacy as the fine-grained measurements in the smart metering system can expose consumer's privacy through the disclosure of accurate load profiles of home energy usage. To address this issue, in this paper we propose novel scaling perturbation based privacy-preserving schemes that can achieve great utility for fine-grained measurements in a privacy-friendly and cost-effective manner. Our schemes adopt the measurement-based scaling perturbation to hide original measurements with low cost. Through a combination of both extensive theoretical analysis and experiments, our results show that the proposed schemes can preserve consumers' privacy through fine-grained measurements and achieve a better utility-privacy tradeoff in comparison with the existing schemes.